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Read Ebook {PDF EPUB} Stealing Utopia by Tilda Booth Silk, Steel and Steam. When Annette discovers the secret her husband will kill to keep, the only safe place is with the man she once rejected. Isaac knows it would be easier to avoid repeating past mistakes by getting rid of the mysterious woman asking for his help - if she wasn't so irresistible. What she is could kill them both. unless Isaac abandons science for a second chance at love. Flavia's Flying Corset by Sahara Kelly Flavia arrives at Dr. Harland Gennaro's castle with no plans to reignite their former passion, but to retrieve what he stole - by any means necessary. After convincing her he's not the guilty party, Harland wastes no time testing the last of her new compound, "Icarus". But a thief watches, waiting for the right moment to strike. Stealing Utopia by Tilda Booth H. George Wells is leading Britain into a Golden Age - then he's kidnapped by a beautiful, passionate adventuress who despises everything he works for. Jane has reason to fear Utopia. Still, she's irresistibly drawn to George even as the future pushes them apart. She must choose between saving the man she loves. or sacrificing him to the cause. Warning: This book contains gadgets, guns, death rays, dirigibles, sexy scientists, mad scientists, wanton murder, identity crises, boiling hot underwater sex and a smoking hot Victorian spy who's as much steam as she is punk. Don't blame us if it makes you want to slip a pistol into your garter and abduct the man of your dreams. Reading this book may stimulate an interest in the principles of physics, aerodynamics and the science of sexual arousal. The authors are not responsible for any injury incurred while investigating all three topics simultaneously. Text Summarization¶ Demonstrates summarizing text by extracting the most important sentences from it. This module automatically summarizes the given text, by extracting one or more important sentences from the text. In a similar way, it can also extract keywords. This tutorial will teach you to use this summarization module via some examples. First, we will try a small example, then we will try two larger ones, and then we will review the performance of the summarizer in terms of speed. This summarizer is based on the , from an “TextRank” algorithm by Mihalcea et al. This algorithm was later improved upon by Barrios et al., by introducing something called a “BM25 ranking function”. Gensim’s summarization only works for English for now, because the text is pre-processed so that stopwords are removed and the words are stemmed, and these processes are language-dependent. Small example¶ First of all, we import the gensim.summarization.summarize() function. We will try summarizing a small toy example; later we will use a larger piece of text. In reality, the text is too small, but it suffices as an illustrative example. To summarize this text, we pass the raw string data as input to the function “summarize”, and it will return a summary. Note: make sure that the string does not contain any newlines where the line breaks in a sentence. A sentence with a newline in it (i.e. a carriage return, “n”) will be treated as two sentences. Use the “split” option if you want a list of strings instead of a single string. You can adjust how much text the summarizer outputs via the “ratio” parameter or the “word_count” parameter. Using the “ratio” parameter, you specify what fraction of sentences in the original text should be returned as output. Below we specify that we want 50% of the original text (the default is 20%). Using the “word_count” parameter, we specify the maximum amount of words we want in the summary. Below we have specified that we want no more than 50 words. As mentioned earlier, this module also supports keyword extraction. Keyword extraction works in the same way as summary generation (i.e. sentence extraction), in that the algorithm tries to find words that are important or seem representative of the entire text. They keywords are not always single words; in the case of multi-word keywords, they are typically all nouns. Larger example¶ Let us try an example with a larger piece of text. We will be using a synopsis of the movie “The Matrix”, which we have taken from this IMDb page. In the code below, we read the text file directly from a web-page using “requests”. Then we produce a summary and some keywords. First, the summary. And now, the keywords: If you know this movie, you see that this summary is actually quite good. We also see that some of the most important characters (Neo, Morpheus, Trinity) were extracted as keywords. Another example¶ Let’s try an example similar to the one above. This time, we will use the IMDb synopsis The Big Lebowski. Again, we download the text and produce a summary and some keywords. This time around, the summary is not of high quality, as it does not tell us much about the movie. In a way, this might not be the algorithms fault, rather this text simply doesn’t contain one or two sentences that capture the essence of the text as in “The Matrix” synopsis. The keywords, however, managed to find some of the main characters. Performance¶ We will test how the speed of the summarizer scales with the size of the dataset. These tests were run on an Intel Core i5 4210U CPU @ 1.70 GHz x 4 processor. Note that the summarizer does not support multithreading (parallel processing). The tests were run on the book “Honest Abe” by Alonzo Rothschild. Download the book in plain-text here. In the plot below , we see the running times together with the sizes of the datasets. To create datasets of different sizes, we have simply taken prefixes of text; in other words we take the first n characters of the book. The algorithm seems to be quadratic in time , so one needs to be careful before plugging a large dataset into the summarizer. Text-content dependent running times¶ The running time is not only dependent on the size of the dataset. For example, summarizing “The Matrix” synopsis (about 36,000 characters) takes about 3.1 seconds, while summarizing 35,000 characters of this book takes about 8.5 seconds. So the former is more than twice as fast . One reason for this difference in running times is the data structure that is used. The algorithm represents the data using a graph, where vertices (nodes) are sentences, and then constructs weighted edges between the vertices that represent how the sentences relate to each other. This means that every piece of text will have a different graph, thus making the running times different. The size of this data structure is quadratic in the worst case (the worst case is when each vertex has an edge to every other vertex). Another possible reason for the difference in running times is that the problems converge at different rates, meaning that the error drops slower for some datasets than for others. Montemurro and Zanette’s entropy based keyword extraction algorithm¶ This paper describes a technique to identify words that play a significant role in the large-scale structure of a text. These typically correspond to the major themes of the text. The text is divided into blocks of. 1000 words, and the entropy of each word’s distribution amongst the blocks is caclulated and compared with the expected entropy if the word were distributed randomly. By default, the algorithm weights the entropy by the overall frequency of the word in the document. We can remove this weighting by setting weighted=False. When this option is used, it is possible to calculate a threshold automatically from the number of blocks. The complexity of the algorithm is O ( Nw ), where N is the number of words in the document and w is the number of unique words. Total running time of the script: ( 0 minutes 16.214 seconds) Bill and Ted Face the Music. It’s been a tough quarter-century for Bill S. Preston, Esquire and Ted “Theodore” Logan. Yes, these friends most triumphantly passed high-school history by traveling through time in a futuristic phone booth and swept a pair of bodacious princesses off their feet while doing it, then Twistered and Battleshipped their way out of the Grim Reaper’s clutches and seemed to usher in utopian paradise through the power of their songcraft. But middle-age ennui is an entirely different air guitar, one that’s tough to keep in tune. That’s the familiar hook on which screenwriters Chris Matheson and Ed Solomon hang Bill and Ted Face the Music. Opening at select theaters on Friday and also available through video-on-demand services, this is Matheson and Solomon’s long-in-the-works capper to a franchise they created in 1988 that also jump-started the careers of Alex Winter, who plays Bill, and Keanu Reeves, who plays Ted. (Among this installment’s many executive producers? Steven Soderbergh, illustrating that there is truly no charitable cinematic gift this man would not try to bestow upon us.) It would suffice for Winter and Reeves to revive their dopily delightful proto-Jay and Silent Bob friendship. The actors are certainly here to endear, but the pair also locks into a soul-sick woe of pals who have pursued their passion and purported purpose for so long with so little to show for it. As the rock ‘n’ roll duo Wyld Stallyns, Bill and Ted believed their ’90s anthem “Those Who Rock” would be their gateway to rock megastardom (and that future utopia).

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